Research On Localization Algorithm Of Wireless Sensor Networks Based On Matrix Reconstruction

Wireless sensor networks are consisted of a large number of micro sensors with wireless communication function.In recent years,many application systems have been built on the basis of wireless sensor networks.Because most of the wireless sensor network applications require the location information of sensor nodes,localization has become a hot research topic in wireless sensor networks.As the existed localization algorithms are sensitive to ranging error and network topology,it is easy to produce large positioning error under the complex environment.The low rank property of the square matrix of Euclidean distance provides the possibility to apply the theory of matrix restoration to the localization of wireless sensor networks.The research work of this paper mainly focuses on how to solve the localization problem of wireless sensor networks based on matrix restoration theory and multidimensional scaling.Aiming at the complex environment that WSNs is low connectivity,limited range information and measurement noise,we put forward positioning algorithm based on extended Lagrange matrix completion.Distance matrix recovery problem is modeled as a matrix completion with Gauss noise based on low rank characteristic of distance matrix between nodes.Through matrix completion,we can recover Incomplete Noisy Distance Measurements and explicitly sift Gaussian noise,reducing the impact of environmental noise and improving the positioning stability and positioning accuracy.Experimental results show that compared with the POSITIONING OPTSPACE algorithm,the proposed algorithm reduces the positioning error of about 31%and 6%,and the positioning stability is better.In view of the traditional location method need large node distance information,and ranging error is caused by multipath effect and noise interference,here we propose a reconstruction algorithm based on DRMF-FPCA.Distance matrix recovery problem is modeled as a matrix completion with Gauss noise and outliers based on low rank characteristic of distance matrix between nodes.The DRMF-FPCA location algorithm that can recover the missing range measurements and explicitly sift Gaussian noise and outlier simultaneously can not only explicitly resolve the abnormal values in the sample matrix,but also the implicit smoothing of the common Gauss random noise.Simulation results show that compared with the existing similar location method,the algorithm just sampling can be realized accurate node localization range and has good anti-interference ability of noise ranging for resource constrained WSNs.